Overview

Dataset statistics

Number of variables9
Number of observations840
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory59.2 KiB
Average record size in memory72.2 B

Variable types

Numeric7
Categorical2

Alerts

Año is highly overall correlated with Fibra ópticaHigh correlation
ADSL is highly overall correlated with Cablemodem and 1 other fieldsHigh correlation
Cablemodem is highly overall correlated with ADSL and 4 other fieldsHigh correlation
Fibra óptica is highly overall correlated with Año and 4 other fieldsHigh correlation
Wireless is highly overall correlated with Cablemodem and 3 other fieldsHigh correlation
Otros is highly overall correlated with Cablemodem and 3 other fieldsHigh correlation
Total is highly overall correlated with ADSL and 5 other fieldsHigh correlation
Provincia is highly overall correlated with TotalHigh correlation
Provincia is uniformly distributedUniform
Cablemodem has 14 (1.7%) zerosZeros
Fibra óptica has 9 (1.1%) zerosZeros
Wireless has 38 (4.5%) zerosZeros

Reproduction

Analysis started2023-07-09 02:04:08.897781
Analysis finished2023-07-09 02:04:29.257214
Duration20.36 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

Año
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.8857
Minimum2014
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-07-08T21:04:29.457333image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014
Q12016
median2018
Q32020
95-th percentile2022
Maximum2022
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.528745
Coefficient of variation (CV)0.0012531656
Kurtosis-1.2039979
Mean2017.8857
Median Absolute Deviation (MAD)2
Skewness0.022512286
Sum1695024
Variance6.3945513
MonotonicityDecreasing
2023-07-08T21:04:29.745669image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2021 96
11.4%
2020 96
11.4%
2019 96
11.4%
2018 96
11.4%
2017 96
11.4%
2016 96
11.4%
2015 96
11.4%
2014 96
11.4%
2022 72
8.6%
ValueCountFrequency (%)
2014 96
11.4%
2015 96
11.4%
2016 96
11.4%
2017 96
11.4%
2018 96
11.4%
2019 96
11.4%
2020 96
11.4%
2021 96
11.4%
2022 72
8.6%
ValueCountFrequency (%)
2022 72
8.6%
2021 96
11.4%
2020 96
11.4%
2019 96
11.4%
2018 96
11.4%
2017 96
11.4%
2016 96
11.4%
2015 96
11.4%
2014 96
11.4%

Trimestre
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
3
216 
2
216 
1
216 
4
192 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters840
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 216
25.7%
2 216
25.7%
1 216
25.7%
4 192
22.9%

Length

2023-07-08T21:04:30.098939image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-08T21:04:30.470579image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
3 216
25.7%
2 216
25.7%
1 216
25.7%
4 192
22.9%

Most occurring characters

ValueCountFrequency (%)
3 216
25.7%
2 216
25.7%
1 216
25.7%
4 192
22.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 840
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 216
25.7%
2 216
25.7%
1 216
25.7%
4 192
22.9%

Most occurring scripts

ValueCountFrequency (%)
Common 840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 216
25.7%
2 216
25.7%
1 216
25.7%
4 192
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 216
25.7%
2 216
25.7%
1 216
25.7%
4 192
22.9%

Provincia
Categorical

HIGH CORRELATION  UNIFORM 

Distinct24
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
Buenos Aires
 
35
Capital Federal
 
35
Catamarca
 
35
Chaco
 
35
Chubut
 
35
Other values (19)
665 

Length

Max length19
Median length15
Mean length8.9166667
Min length5

Characters and Unicode

Total characters7490
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBuenos Aires
2nd rowCapital Federal
3rd rowCatamarca
4th rowChaco
5th rowChubut

Common Values

ValueCountFrequency (%)
Buenos Aires 35
 
4.2%
Capital Federal 35
 
4.2%
Catamarca 35
 
4.2%
Chaco 35
 
4.2%
Chubut 35
 
4.2%
Córdoba 35
 
4.2%
Corrientes 35
 
4.2%
Entre Ríos 35
 
4.2%
Formosa 35
 
4.2%
Jujuy 35
 
4.2%
Other values (14) 490
58.3%

Length

2023-07-08T21:04:30.823166image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
santa 70
 
5.3%
la 70
 
5.3%
del 70
 
5.3%
san 70
 
5.3%
buenos 35
 
2.6%
negro 35
 
2.6%
salta 35
 
2.6%
juan 35
 
2.6%
luis 35
 
2.6%
cruz 35
 
2.6%
Other values (24) 840
63.2%

Most occurring characters

ValueCountFrequency (%)
a 980
 
13.1%
e 595
 
7.9%
o 525
 
7.0%
490
 
6.5%
r 455
 
6.1%
n 455
 
6.1%
u 455
 
6.1%
t 350
 
4.7%
s 315
 
4.2%
i 315
 
4.2%
Other values (30) 2555
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5670
75.7%
Uppercase Letter 1330
 
17.8%
Space Separator 490
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 980
17.3%
e 595
10.5%
o 525
9.3%
r 455
8.0%
n 455
8.0%
u 455
8.0%
t 350
 
6.2%
s 315
 
5.6%
i 315
 
5.6%
l 175
 
3.1%
Other values (15) 1050
18.5%
Uppercase Letter
ValueCountFrequency (%)
C 245
18.4%
S 210
15.8%
F 140
10.5%
L 105
7.9%
R 105
7.9%
J 70
 
5.3%
M 70
 
5.3%
E 70
 
5.3%
D 70
 
5.3%
T 70
 
5.3%
Other values (4) 175
13.2%
Space Separator
ValueCountFrequency (%)
490
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7000
93.5%
Common 490
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 980
14.0%
e 595
 
8.5%
o 525
 
7.5%
r 455
 
6.5%
n 455
 
6.5%
u 455
 
6.5%
t 350
 
5.0%
s 315
 
4.5%
i 315
 
4.5%
C 245
 
3.5%
Other values (29) 2310
33.0%
Common
ValueCountFrequency (%)
490
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7315
97.7%
None 175
 
2.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 980
13.4%
e 595
 
8.1%
o 525
 
7.2%
490
 
6.7%
r 455
 
6.2%
n 455
 
6.2%
u 455
 
6.2%
t 350
 
4.8%
s 315
 
4.3%
i 315
 
4.3%
Other values (26) 2380
32.5%
None
ValueCountFrequency (%)
í 70
40.0%
é 35
20.0%
ó 35
20.0%
á 35
20.0%

ADSL
Real number (ℝ)

HIGH CORRELATION 

Distinct748
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127566.81
Minimum6842
Maximum1586343
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-07-08T21:04:31.194829image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum6842
5-th percentile11107.8
Q122479.25
median48596
Q3104569.5
95-th percentile447847.45
Maximum1586343
Range1579501
Interquartile range (IQR)82090.25

Descriptive statistics

Standard deviation255054.3
Coefficient of variation (CV)1.9993782
Kurtosis19.779528
Mean127566.81
Median Absolute Deviation (MAD)28916
Skewness4.2804047
Sum1.0715612 × 108
Variance6.5052698 × 1010
MonotonicityNot monotonic
2023-07-08T21:04:31.609365image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22636 9
 
1.1%
11432 9
 
1.1%
48670 9
 
1.1%
12754 7
 
0.8%
40072 4
 
0.5%
8417 4
 
0.5%
12347 4
 
0.5%
12853 4
 
0.5%
43994 3
 
0.4%
12845 3
 
0.4%
Other values (738) 784
93.3%
ValueCountFrequency (%)
6842 1
 
0.1%
6860 2
0.2%
7759 1
 
0.1%
7796 2
0.2%
7987 1
 
0.1%
8003 1
 
0.1%
8175 1
 
0.1%
8301 1
 
0.1%
8401 2
0.2%
8417 4
0.5%
ValueCountFrequency (%)
1586343 1
0.1%
1585467 1
0.1%
1583560 1
0.1%
1583135 1
0.1%
1581770 1
0.1%
1579448 1
0.1%
1575978 1
0.1%
1574216 1
0.1%
1568881 1
0.1%
1567685 1
0.1%

Cablemodem
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct729
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171775.05
Minimum0
Maximum2748325
Zeros14
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-07-08T21:04:32.075057image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile451
Q18599.75
median34984
Q370748.5
95-th percentile1096875.9
Maximum2748325
Range2748325
Interquartile range (IQR)62148.75

Descriptive statistics

Standard deviation422166.1
Coefficient of variation (CV)2.4576684
Kurtosis15.406709
Mean171775.05
Median Absolute Deviation (MAD)28031
Skewness3.7858017
Sum1.4429104 × 108
Variance1.7822421 × 1011
MonotonicityNot monotonic
2023-07-08T21:04:32.455774image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34984 15
 
1.8%
0 14
 
1.7%
451 14
 
1.7%
13030 13
 
1.5%
3900 7
 
0.8%
2722 5
 
0.6%
4365 4
 
0.5%
1025 4
 
0.5%
40205 3
 
0.4%
1120 3
 
0.4%
Other values (719) 758
90.2%
ValueCountFrequency (%)
0 14
1.7%
46 1
 
0.1%
83 1
 
0.1%
97 3
 
0.4%
100 1
 
0.1%
115 1
 
0.1%
241 2
 
0.2%
242 3
 
0.4%
243 2
 
0.2%
244 1
 
0.1%
ValueCountFrequency (%)
2748325 1
0.1%
2728865 1
0.1%
2719613 1
0.1%
2706506 1
0.1%
2595485 1
0.1%
2503830 1
0.1%
2452056 1
0.1%
2441248 1
0.1%
2384557 1
0.1%
2244277 1
0.1%

Fibra óptica
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct592
Distinct (%)70.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29911.862
Minimum0
Maximum1436433
Zeros9
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-07-08T21:04:32.925023image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q1186
median1093
Q313862.5
95-th percentile120987.3
Maximum1436433
Range1436433
Interquartile range (IQR)13676.5

Descriptive statistics

Standard deviation123501.26
Coefficient of variation (CV)4.128839
Kurtosis69.410709
Mean29911.862
Median Absolute Deviation (MAD)1079
Skewness7.8221438
Sum25125964
Variance1.5252561 × 1010
MonotonicityNot monotonic
2023-07-08T21:04:33.352271image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 17
 
2.0%
16 13
 
1.5%
116 13
 
1.5%
15 12
 
1.4%
22 12
 
1.4%
354 11
 
1.3%
14 10
 
1.2%
916 10
 
1.2%
23 9
 
1.1%
0 9
 
1.1%
Other values (582) 724
86.2%
ValueCountFrequency (%)
0 9
1.1%
1 6
0.7%
2 2
 
0.2%
4 4
0.5%
5 2
 
0.2%
6 8
1.0%
9 1
 
0.1%
11 3
 
0.4%
12 3
 
0.4%
13 5
0.6%
ValueCountFrequency (%)
1436433 1
0.1%
1399043 1
0.1%
1242121 1
0.1%
1176024 1
0.1%
885613 1
0.1%
854173 1
0.1%
821597 1
0.1%
804991 1
0.1%
749087 1
0.1%
723072 1
0.1%

Wireless
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct592
Distinct (%)70.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10345.001
Minimum0
Maximum126887
Zeros38
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-07-08T21:04:33.817168image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q1253
median4261.5
Q312435
95-th percentile53097
Maximum126887
Range126887
Interquartile range (IQR)12182

Descriptive statistics

Standard deviation18192.024
Coefficient of variation (CV)1.7585328
Kurtosis14.100261
Mean10345.001
Median Absolute Deviation (MAD)4209.5
Skewness3.4353808
Sum8689801
Variance3.3094974 × 108
MonotonicityNot monotonic
2023-07-08T21:04:34.329695image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38
 
4.5%
1 36
 
4.3%
5 13
 
1.5%
2 11
 
1.3%
52 8
 
1.0%
1354 7
 
0.8%
911 6
 
0.7%
1023 6
 
0.7%
158 5
 
0.6%
27 5
 
0.6%
Other values (582) 705
83.9%
ValueCountFrequency (%)
0 38
4.5%
1 36
4.3%
2 11
 
1.3%
3 2
 
0.2%
5 13
 
1.5%
6 1
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
13 4
 
0.5%
14 4
 
0.5%
ValueCountFrequency (%)
126887 1
0.1%
126847 1
0.1%
126846 1
0.1%
125521 1
0.1%
120228 1
0.1%
113546 1
0.1%
98806 1
0.1%
94162 1
0.1%
93444 1
0.1%
91736 1
0.1%

Otros
Real number (ℝ)

HIGH CORRELATION 

Distinct561
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6142.9619
Minimum2
Maximum73415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-07-08T21:04:34.811275image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile28
Q1307.25
median1852
Q36915.5
95-th percentile29236.05
Maximum73415
Range73413
Interquartile range (IQR)6608.25

Descriptive statistics

Standard deviation10572.153
Coefficient of variation (CV)1.7210188
Kurtosis10.659989
Mean6142.9619
Median Absolute Deviation (MAD)1801.5
Skewness2.9254191
Sum5160088
Variance1.1177042 × 108
MonotonicityNot monotonic
2023-07-08T21:04:35.337819image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34 15
 
1.8%
35 14
 
1.7%
41 11
 
1.3%
30 11
 
1.3%
31 8
 
1.0%
2350 8
 
1.0%
28 8
 
1.0%
15 7
 
0.8%
43 6
 
0.7%
8 6
 
0.7%
Other values (551) 746
88.8%
ValueCountFrequency (%)
2 1
 
0.1%
7 1
 
0.1%
8 6
0.7%
9 4
0.5%
11 1
 
0.1%
12 1
 
0.1%
13 5
0.6%
14 5
0.6%
15 7
0.8%
18 1
 
0.1%
ValueCountFrequency (%)
73415 1
0.1%
71028 1
0.1%
70416 1
0.1%
66872 1
0.1%
64554 1
0.1%
57927 1
0.1%
57864 1
0.1%
57547 1
0.1%
57189 1
0.1%
55541 1
0.1%

Total
Real number (ℝ)

HIGH CORRELATION 

Distinct831
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean345741.69
Minimum12557
Maximum4721668
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-07-08T21:04:35.767269image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum12557
5-th percentile25711.3
Q152029.75
median103489.5
Q3177914.75
95-th percentile1415716
Maximum4721668
Range4709111
Interquartile range (IQR)125885

Descriptive statistics

Standard deviation741943.58
Coefficient of variation (CV)2.1459477
Kurtosis14.446647
Mean345741.69
Median Absolute Deviation (MAD)56930.5
Skewness3.7362303
Sum2.9042302 × 108
Variance5.5048027 × 1011
MonotonicityNot monotonic
2023-07-08T21:04:36.414892image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32557 3
 
0.4%
13307 3
 
0.4%
34405 2
 
0.2%
117903 2
 
0.2%
51968 2
 
0.2%
13232 2
 
0.2%
170823 2
 
0.2%
89050 1
 
0.1%
97390 1
 
0.1%
63765 1
 
0.1%
Other values (821) 821
97.7%
ValueCountFrequency (%)
12557 1
0.1%
13046 1
0.1%
13055 1
0.1%
13147 1
0.1%
13218 1
0.1%
13219 1
0.1%
13221 1
0.1%
13232 2
0.2%
13256 1
0.1%
13293 1
0.1%
ValueCountFrequency (%)
4721668 1
0.1%
4667183 1
0.1%
4555424 1
0.1%
4509157 1
0.1%
4251609 1
0.1%
4132351 1
0.1%
4060002 1
0.1%
4033261 1
0.1%
3960233 1
0.1%
3937277 1
0.1%

Interactions

2023-07-08T21:04:25.684114image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:11.372553image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:13.495951image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:15.562317image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:17.430588image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:19.735834image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:22.687606image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:26.046550image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:11.700669image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:13.878088image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:15.787655image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:17.773564image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:20.419564image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:23.299851image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:26.577369image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:11.999747image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:14.173062image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:16.084590image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:18.058435image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:21.002357image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:23.703240image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:26.928515image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:12.301181image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:14.459575image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:16.353862image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:18.345799image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:21.371583image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:24.126462image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:27.405032image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:12.592171image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:14.747343image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:16.584221image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:18.632770image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:21.743446image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:24.549646image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:27.698629image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:12.944752image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:15.067796image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:16.902278image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:18.862769image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:22.087535image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:24.965667image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:28.053517image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:13.266317image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:15.301743image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:17.175633image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:19.265175image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:22.382381image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:04:25.327512image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-07-08T21:04:36.721051image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
AñoADSLCablemodemFibra ópticaWirelessOtrosTotalTrimestreProvincia
Año1.000-0.1460.4170.5320.4850.4060.3010.0000.000
ADSL-0.1461.0000.5370.4000.2050.2880.7970.0000.475
Cablemodem0.4170.5371.0000.6840.5560.5030.8350.0000.472
Fibra óptica0.5320.4000.6841.0000.6700.6540.6890.0000.211
Wireless0.4850.2050.5560.6701.0000.6200.5580.0000.356
Otros0.4060.2880.5030.6540.6201.0000.5510.0000.379
Total0.3010.7970.8350.6890.5580.5511.0000.0000.577
Trimestre0.0000.0000.0000.0000.0000.0000.0001.0000.000
Provincia0.0000.4750.4720.2110.3560.3790.5770.0001.000

Missing values

2023-07-08T21:04:28.511753image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-08T21:04:29.047722image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AñoTrimestreProvinciaADSLCablemodemFibra ópticaWirelessOtrosTotal
020223Buenos Aires33964827483251436433126846704164721668
120223Capital Federal14079112401251292185758317871547679
220223Catamarca1001010495462241329223570293
320223Chaco27164618004464581782359144146
420223Chubut45377722129574297848831165778
520223Córdoba1637704654613385706107097971038668
620223Corrientes36508771401861972565323144846
720223Entre Ríos60345132952369712609812593268959
820223Formosa1447025880110411693521268538
920223Jujuy19257529784017240332383118823
AñoTrimestreProvinciaADSLCablemodemFibra ópticaWirelessOtrosTotal
83020141Neuquén477902816199710381305190380
83120141Río Negro64886241568763576157295066
83220141Salta731311753845856891290
83320141San Juan48161434722808117351298
83420141San Luis113064283542144812557
83520141Santa Cruz189271003814092326426764
83620141Santa Fe32266217429630595951644506612
83720141Santiago Del Estero325673598199153437133
83820141Tierra Del Fuego2161828376481293428038
83920141Tucumán129717831211398130032